A study of optimization of injection molding process parameters for SGF and PTFE reinforced PC composites using neural network and response surface methodology

Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO

“…Subsequently, Chen et al [23] developed an approach in a soft computing paradigm for the process parameter optimization of the multiple-input and multiple-output (MIMO) plastic injection molding process, which integrated Taguchi's parameter design method, BPNN, GA, and engineering optimization concepts. Furthermore, Tzeng et al [24] constructed a hybrid method in conjunction with Taguchi orthogonal array experiments, ANOVA, RSM, BPNN, and GA to predict the quality characteristics of SGF-and PTFE-reinforced PC composites, such as ultimate strength, flexural strength, and impact resistance, and finally generate an optimal parameter setting of the injection molding process under a MIMO consideration. Recently, Huang et al [25] proposed a hybrid optimization approach integrating BPNN with embedded SA into the GA to improve its local searching ability algorithm and optimize the thickness of the blow-molded polypropylene bellows used in cars.…”

Performance properties and applications of polytetrafluoroethylene (PTFE)—a review

“…PTFE reinforced with short glass fibers (SGF) were optimized for injection molding. On injection of PTFE/SGF, the parameters mentioned above can significantly increase the strength and impact resistance [53].…”

Process parameter optimization of plastic injection molding: a review

“…Many such hybrid techniques have been investigated to injection molding optimization also, like GA with the Taguchi method for minimizing warpage of molded components [98], GA with ANN for optimizing the initial process settings [81], genetic neural fuzzy system with 2-stage hybrid learning algorithm to predict product weight [63,64], GA with BPNN to achieve the optimal quality in terms of shear stress [101] and to minimize volumetric shrinkage [100], the Taguchi method combined with ANN and GA to achieve the minimal single response output in terms of warpage in a bus ceiling lamp base [55] and to save energy by multi-objective optimization of process parameters [70], the Taguchi method combined with BPNN and GA to determine the set of data in multiple-input single-output (MISO) by optimizing product weight [17] and to achieve multi response outputs [20], the Taguchi method and response surface method combined with BPNN and GA for predicting mechanical properties by estimating an optimal set of process parameters [110], the Taguchi method with Moldflow ® for finding the efficient frontier for a thin digital camera cover in a MIMO environment [24], Moldflow ® and orthogonal experiment method integrated with BPNN and GA to determine the optimal set of process parameters for optimizing warpage and clamp force [122], the variable complexity method combined with BPNN and GA to mice manufacturing for optimizing multiple objectives [26], GA with response surface methodology to achieve the optimal single response in terms of warpage in thin shell plastic parts [54] and to minimize sink depth in thermoplastic components [75], simulated annealing with ANN to predict part warpage in runner system by optimizing the runner dimensions [121], GA with a gradient-based method to find the optimum process parameters [59], PSO with ANN to optimize process parameters [103], BPNN with the Taguchi method and Davidson-Fletcher-Powell method to determine multiple input process parameters in order to achieve the desired product weight as the single output [18,19], the Latin hypercube sampling method combined with the Kriging method and multi-objective PSO to achieve a better Pareto frontier by reducing simulation cost [21], the response surface method integrated with Moldflow ® and Lingo software to optimize process parameters with corresponding output of warpage and shrinkage [22], GA with the mode-pursuing sampling method for achieving minimum warpage [30], ANN and artificial bee colony algorithm to determine the set of process parameters by minimizing warpage of molded components [42], the Taguchi method ...…”